Fall detector incorporating physiological sensing

ABSTRACT

A method for detecting a fall by a user wearing a fall detector, including: detecting a trigger event identifying the time location of a possible fall event in user data; extracting motion features from motion data and physiological features from physiological data from within a time window around the identified time location; and determining whether the detected trigger event is a fall by the user by inputting the at least one of the motion features and at least one of the physiological features into a classifier.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/324,843 filed May 19, 2021 which claims the benefit of U.S.Provisional Application No. 63/027,392, filed on 20 May 2020. Thisapplication is hereby incorporated by reference herein.

TECHNICAL FIELD

Various exemplary embodiments disclosed herein relate generally to asystem and method for detecting falls using physiological measurementssuch as for example heart rate and skin conductance sensing.

BACKGROUND

Wearable devices that host automatic fall detection usually make use ofestimated values of height changes, impacts, and possibly orientationchanges. Sensors that provide the signals to perform fall estimationinclude air pressure sensors, accelerometers, gyroscopes andmagnetometers. Typically, the estimated feature values are combined in aclassifier, which decides whether the event is a fall or a non-fall. Theaccuracy of the classifier depends on the distinguishing power of thesefeature values. The distinguishing power relates to the distribution ofpossible values that may happen during a fall and during non-fallevents. The less the fall and non-fall distributions overlap, the betterthe accuracy. In general, at body torso locations these distributionsare often sufficiently different between falls and non-fall events torealize high accuracy detection outcomes.

SUMMARY

A summary of various exemplary embodiments is presented below. Somesimplifications and omissions may be made in the following summary,which is intended to highlight and introduce some aspects of the variousexemplary embodiments, but not to limit the scope of the invention.Detailed descriptions of an exemplary embodiment adequate to allow thoseof ordinary skill in the art to make and use the inventive concepts willfollow in later sections.

Various embodiments relate to a method for detecting a fall by a userwearing a fall detector, including: detecting a trigger eventidentifying the time location of a possible fall event in user data;extracting motion features from motion data and physiological featuresfrom physiological data from within a time window around the identifiedtime location; and determining whether the detected trigger event is afall by the user by inputting the at least one of the motion featuresand at least one of the physiological features into a classifier.

Various embodiments are described, wherein the motion data includes oneof acceleration data, height data, angular velocity data, andacceleration data and height data.

Various embodiments are described, wherein the motion data includes datafrom an accelerometer.

Various embodiments are described, wherein the physiological datainclude one of heart rate data, skin conductance data, and heart rateand skin conductance data.

Various embodiments are described, wherein extracting motion featuresand physiological features further includes: determining a firstphysiological data value at a first time before the trigger event;determining a second physiological data value at the trigger event;determining a third physiological data value at a third time after thetrigger event, wherein at least one physiological feature is based upona difference between two of the first, second, and third physiologicaldata values.

Various embodiments are described, wherein the physiological featuresare based upon a difference between the first and second physiologicalvalues, the second and third physiological values, the first and thirdphysiological values, and second physiological value and one half thesum of the first and third physiological feature.

Various embodiments are described, wherein detecting an impact basedupon motion data further includes determining that a change inacceleration over a specified time exceeds a threshold value.

Various embodiments are described, further including determining thatextracted motion features are outside a specified normal range of valuesand then determining that the impact is not a fall by the user.

Various embodiments are described, further including when a fall isindicated, receiving an output from an exception machine learningclassifier that indicates that the impact is not a fall.

Various embodiments are described, wherein the machine learningclassifier includes: a motion classifier that determines whether theimpact is a fall, a non-fall, or undetermined based upon the extractedmotion features and a first threshold value and a second thresholdvalue; and a physiological classifier that determines whether the impactis a fall or a non-fall based upon both the extracted motion featuresand the extracted physiological features when the output of the motionclassifier is undetermined.

Various embodiments are described, further including receiving thephysiological data from a remote sensor.

Further various embodiments relate to a fall detector for detecting afall by a user wearing the fall detector, including: a trigger deviceconfigured to detect an trigger event identifying the time of a possiblefall event in user data; a feature extractor configured to extractmotion features from motion data and physiological features fromphysiological data from within a time window around the identified timelocation; and a machine learning classifier configured to determinewhether the detected trigger event is a fall by the user based upon theat least one of the motion features and at least one of thephysiological features.

Various embodiments are described, wherein the motion data includes oneof acceleration data, height data, angular velocity data, andacceleration data and height data.

Various embodiments are described, further including an accelerometerconfigured to produce a portion of the motion data.

Various embodiments are described, further including on or morephysiological sensors configured to produce the physiological dataincluding one of heart rate data, skin conductance data, and heart rateand skin conductance data.

Various embodiments are described, wherein extracting motion featuresand physiological features further includes: determining a firstphysiological data value at a first time before the trigger event;determining a second physiological data value at the trigger event;determining a third physiological data value at a third time after thetrigger event, wherein at least one physiological feature is based upona difference between two of the first, second, and third physiologicaldata values.

Various embodiments are described, wherein the physiological featuresare based upon a difference between the first and second physiologicalvalues, the second and third physiological values, the first and thirdphysiological values, and second physiological value and one half thesum of the first and third physiological feature.

Various embodiments are described, wherein detecting an impact basedupon motion data further includes determining that a change inacceleration over a specified time exceeds a threshold value.

Various embodiments are described, wherein the feature extractor isfurther configured to determine that extracted motion features areoutside a specified normal range of values and then determining that theimpact is not a fall by the user.

Various embodiments are described, further including an exceptionhandler configured to receive an output from an exception machinelearning classifier that indicates that the impact is not a fall, when afall is indicated by the machine learning classifier.

Various embodiments are described, wherein the machine learningclassifier includes: a motion classifier that determines whether theimpact is a fall, a non-fall, or undetermined based upon the extractedmotion features and a first threshold value and a second thresholdvalue; and a physiological classifier that determines whether the impactis a fall or a non-fall based upon both the extracted motion featuresand the extracted physiological features when the output of the motionclassifier is undetermined.

Various embodiments are described, further including a communicationinterface configured to receive the physiological data from a remotesensor.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better understand various exemplary embodiments, referenceis made to the accompanying drawings, wherein:

FIG. 1 illustrates a scatter plot of data points indicatingfall/non-fall events with wrist rise and height drop as the features;

FIG. 2 illustrates another view of the data using a densitydistribution, i.e., how many events exhibit a certain feature value;

FIG. 3 illustrates a plot of ROC curves for a classifier using onlymotion data and a classifier using motion and physiological data;

FIG. 4 illustrates the flow of the fall detection process carried out bythe fall detector; and

FIG. 5 illustrates an exemplary hardware diagram for the fall detector.

To facilitate understanding, identical reference numerals have been usedto designate elements having substantially the same or similar structureand/or substantially the same or similar function.

DETAILED DESCRIPTION

The description and drawings illustrate the principles of the invention.It will thus be appreciated that those skilled in the art will be ableto devise various arrangements that, although not explicitly describedor shown herein, embody the principles of the invention and are includedwithin its scope. Furthermore, all examples recited herein areprincipally intended expressly to be for pedagogical purposes to aid thereader in understanding the principles of the invention and the conceptscontributed by the inventor(s) to furthering the art and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Additionally, the term, “or,” as used herein,refers to a non-exclusive or (i.e., and/or), unless otherwise indicated(e.g., “or else” or “or in the alternative”). Also, the variousembodiments described herein are not necessarily mutually exclusive, assome embodiments can be combined with one or more other embodiments toform new embodiments.

When fall detection sensors are located on the torso of a user, the falldetection accuracy may be good. However, when the fall detector sensorsare located elsewhere, for example at the wrist, it is more difficult todetect a fall as many normal movements by an arm may appear to be afall. This is because there is more overlap between the variousparameter values for the fall and non-fall situations. For example,height changes may happen due to wrist movements, without the bodyfalling. As another example, this may also be true for orientationchanges. Further, a wrist may hit some furniture or movements like handwaving and hand clapping may happen. These may appear to be impacts thatare similar to a fall. Therefore, a need remains for improving thedetection accuracy when differentiating between fall and non-fall eventsbecomes difficult using various motion measurements. Current detectionaccuracies may require the user to regularly cancel false alarms thatoccur. This might be annoying to the user and may lead to a reducedadherence in wearing the device. The false alarms also burden thehealthcare system by causing the workload of healthcare staff and costsof providing healthcare to increase.

Embodiments of a fall detection system are described herein that improvethe detection accuracy of falls. The fall detection system is based onadding additional dimension(s) to the sensed feature space in order toimprove the overall distinguishing power of the system. In addition tothe mentioned physical characteristics of a fall, physiologicalmeasurements such as heart rate and skin conductivity may be used. It isknown that the autonomous nervous system responds to stressfulsituations. For example, when a person encounters an unexpectedsituation and need to act quickly their heart rate and the skinconductivity will increase. This may happen at simple situations likefood boiling over, or about missing to catch the train. As can beexpected, it has been found this indeed also happens when a person fallsunintentionally. Heart rate can be measured using various sensors suchas a photoplethysmogram (PPG) or an electrocardiogram (ECG) sensor. ThePPG sensor is an optical sensor that detects the change in blood volumenear the sensor that is indicative of the user's pulse rate and hence(in general) heart rate. Accordingly, the PPG sensor can be placed atvarious locations on the body to determine a user's heart rate. The ECGsensor measures small electrical changes that indicate the beating ofthe user's heart. ECG sensors need to be placed near the heart in orderto be effective. Further, skin conductivity may be measured via galvanicskin response. During non-fall events, these physiological values willnot quickly increase in the same way as when a user falls.

Fall data was collected for a number of individuals known to haveproblems with frequent falls. In addition to typical motion relatedparameters, the individual's heart rate was also measured. It wasobserved that during and right after the fall occurs there is anincrease in the individual's pulse rate. For example, the pulse rate mayincrease by order 10% over the pre-fall heart rate. Then fairly quickly,in say about 10 seconds, the individual's pulse rate tend to drop backto near its pre-fall value. This pattern in the change of the heart ratewhen an individual falls can be used to further determine if a fall hastruly occurred, allowing for increased ability to differentiation falland non-fall events. Another observation found that sometimes a fall waspreceded by a lower than normal heart rate. Such a lower/reduced heartrate may be the, or contributing, cause of the individual's fall and isanother parameter that may be used to assist in detecting falls.

Likewise, it has been observed that when an individual falls there is aspike in the individual's skin conductivity, which is typically due toincrease sweating by the individual. After the fall, the skinconductivity does decrease back to near the pre-fall value, but it doesmore gradually than for example the heart rate.

The physiological features themselves do not provide distinguishingpower regarding detecting falls. A change in heart rate or skinconductivity happens for many reasons and not only because of falls.Falls can still be detected, but at a low accuracy. However, bycombining the physiological features with the physical features, thecombined set of data leads to an improved discrimination between fallsand non-falls because the additional data helps to separate thedistributions of the falls and non-falls in the parameter space. In thisway, the false alarm rate may be reduced, while maintaining a gooddetection sensitivity to actual falls.

Upon the occurrence of a trigger event, which will be further describedbelow, the feature values may be computed such as the traditional motionrelated features like height change, impact, and orientation change, butin addition, physiological parameter(s) may be computed such as theheart rate and/or skin conductivity. More precisely, the heart rate maybe determined shortly before the impact of the event (value_1), shortlyafter the fall (value_2), and a short time later (value_3). In oneexample the time of value_1 is 2-5 seconds before the impact, value_2 isabout 5 seconds after the impact, and value_3 is about 10 seconds afterthe impact. Other times may be used as well. For example, the value atthe impact can be used. Similar times may be used for skin conductancevalues, but value_3 may be longer than 10 seconds as the skinconductance value gradually returns to normal after a fall. These may beused to characterize the heart rate change as follows:HR_change_1=value_2−value_1HR_change_2A=value_3−value_1HR_change_2B=value_2−value_3HR_change_3=value_2−(value_1+value_3)/2In another finding, people who fell had a heart rate that was lower thanwhen they did not fall. So the absolute heart rate just before the fallmay be used as well because of this finding because if the heart ratevalue just around the fall is low that might indicate a fall. Similarvalues may be calculated for skin conductance. These values along withthe other motion features values are input into a classifier. Some orall of these physiological values or other modified versions in anycombination may be used in the classifier. When including thephysiologically related feature values, some of the motion relatedfeatures may be discarded, or others can be added. The selected set isto be optimized for best classification accuracy. Correlations betweenthe feature values influence which combination is optimal. A featurevalue that is highly correlated to another may not add much additionaldiscriminating value, but can raise the total noise, and therefore it isbetter to select one of the two.

The classifier is a system known in the field of machine learning. Thebinary classifier is equivalent to a detector. A classifier takes asinput a set of feature values and gives as an output the class orclasses to which this set of features belong. This output may be interms of a full membership (i.e., in or out the class) or as a partialmembership value, such as a probability value. In the case of a binaryclassification, there are two classes (hence one being the complement ofthe other), and this type of classifier is equivalent to a detector.Detection theory stems from the field of signal processing.

Before the classifier is deployed, the classifier is trained: theclassifier is presented with example sets of feature values that arelabeled with the class they belong to. In the training procedure thecurrent classifier iterates over the example sets. For each subsequentfeature-value set, it computes the classification, which output iscompared with the given labeling. The error that is determined from thiscomparison is used to adapt the classifier's internal parameters (orweights) such that the error is expected to be minimized based upon theadapted parameters. For example, in the case of neural networks, atypical optimization algorithm is gradient descent.

There are many forms of classifiers. Popular examples include supportvector machines (SVM), decision tree, random forest, naive Bayesianclassifier (NBC), neural networks including deep learning techniques,logistic regression, k-nearest neighbors (k-NN), etc.

One way to view a classifier is that it tries to find decisionboundaries in the space spanned by the feature set. Every feature in thefeature set is associated with a dimension in that space. So, a givenexample feature-value set (vector) constitutes a point in that space.The classifier partitions that space in subspaces, where each subspaceassociates with a class from the output. So, given a feature-value set,i.e., a point in the feature space, its class is determined from thesubspace in which that feature point (vector) is located. Duringtraining, the boundaries between the subspaces are optimized for optimalclassification.

In detection theory the typical case is to determine whether a signal ispresent in a continuously present noise background. A famous theorem, byNeyman and Pearson, states that for a given (chosen) false alarm rate,the most powerful test to decide whether the signal is present, is thelikelihood ratio test (LRT). In the case of fall detection, thelikelihood ratio equals

${{LR} = \frac{p\left( {x{❘{Fall}}} \right)}{\left. {{{p\left( x \right.}❘}{nonFall}} \right)}},$where P(x|Fall) is the likelihood the given feature values x are by afall, and P(x|nonFall) is the likelihood the given feature values x areby a non-fall.

The LRT tests, for a given set x (i.e., observed/measured set of featurevalues), whether LR≥θ, or not, where θ is the detection thresholdconfigured by the designer of the fall detector. The NP-theorem statesthat for a given false alarm rate (=the number of falsely detectedfalls, i.e., in fact non-fall events) the detection sensitivity (=thefraction of actual falls that gets detected) is maximal. No other testcan improve on that.

The NBC classifier implements the LRT, albeit with the (Naive)assumption that the feature values are independent of each other. Hence,the NBC classifier is a commonly used classifier.

FIG. 1 illustrates a scatter plot of data points indicatingfall/non-fall events with wrist rise and height drop as the features.Only two features are used in this example to simplify the explanationand visualization of the classification process. The vertical axis plotsthe value of wrist rise (relative to the shoulder), and the horizontalaxis plots the value of height drop of the patient. The darker circlesillustrate examples of falls, and the lighter circles illustrateexamples of non-falls. The lines depict different potential detectionboundaries. The line 105 classifies everything with a wrist rise above−0.75 and a height drop less than 0 as a fall. As can be seen, no fallsare missed, but also many false alarms will happen. The line 110 yieldsa better classifier while reducing the number of false alarms, at theexpense of missing a few falls. The curved line 115 yields the bestperformance among these examples, by further reducing the number offalse alarms. The training of the classifier seeks to set the detectionboundary in an optimized way based upon the training data.

FIG. 2 illustrates another view of the data using a densitydistribution, i.e., how many events exhibit a certain feature value. Forease of visualization a 1-dimensional feature set is chosen, or,abstractly, the ordinate represents a vector.

The density curves P(x|Fall) 210 and P(x|nonFall) 205 are depicted,respectively. That is the likelihood of a fall/non-fall for each of thefeature vectors x. The decision threshold 220 is also illustrated as avertical line. This threshold 220 corresponds with the boundary plane inthe feature space of FIG. 1 . FIG. 2 also illustrates how detectionperformance can be evaluated: i.e., integrating the curves from left upto the threshold yields the false negative (FN) 230 percentage and thetrue negative (TN) 225 percentage, respectively; and integrating thecurves from the threshold up to the right yields the true positive (TP)235 percentage and false positive (FP) 240 percentage, respectively. Ascan be seen, there is a region of the features space 215 where confusionresults because the two distributions overlap. Typically the thresholdvalue 220 will fall in this area of confusion 215 and will result insome false negatives (i.e., actual falls being classified as non-falls)and some false positives (i.e., actual non-falls being classified asfalls). As this threshold value varies there is a tradeoff between theprobability of missed falls and the false alarm rate. When thesedistributions can be separated from one another to reduce the overlap,the performance of the classifier improves as the two different classesare more clearly distinguishable.

When plotting the true positive rate (TPR=TP/(TP+FN)) on a vertical axisagainst the false positive rate (FPR=FP/(TN+FP)) on a horizontal axisthe so-called receiver operating characteristics (ROC) curve results.The ideal ROC curve would follow the vertical axis and then follow thevalue of 1.0 for TPR across the plot. When the two distribution curvesof FIG. 2 have less overlap, the ROC curve moves to left-upper corner(it always starts at (0,0) and ends at (1,1)). The overlap between thedistributions may be reduced when either the two distributions (theirmeans) move apart from each other or when the spread of the distributioncurves reduces (the variance reduces), or when both of these happen. Aspreviously stated, the distribution curve is to be understood to be thecombined effect of all features in the selected set. FIG. 3 illustratesthe plot of the ROC for a classifier using only motion data and aclassifier using both motion data and physiological data. Specifically,in FIG. 3 , the lower curve 310 is the performance of an exampleclassifier using only the motion data. The upper curve 305 includes theuse of heart rate information, and because this curve 305 is higher, itshows that the addition of the physiological information improves theperformance of the classifier.

The addition of the physiological features (heart rate and/or skinconductivity), will cause the distributions of falls and non-falls toseparate and/or sharpen, to result in better classifier performance.This was shown in the ROC curve of FIG. 3 .

FIG. 4 illustrates the flow of the fall detection process carried out bythe fall detector. The fall detector has a continuously running process,called trigger 405 that observes the motion signals for a possible fall.In another embodiment, the trigger 405 may look for a spike in the skinconductivity. Other events may be used as trigger events as well. Thetrigger may be implemented as a dedicated specific circuit, for example,using threshold signaling capabilities that state-of-the-artaccelerometers provide or as software instructions running on aprocessor. For example, the accelerometer signals are processed todetect a potential impact. An impact may be determined when theacceleration measured by the accelerometer spikes above a thresholdvalue, but other schemes are also conceivable, for example testingwhether the energy in the acceleration signal passes a threshold, etc.The accelerometer signals may be sampled, for example, at a 50 Hz rate(but other rates may be used as well). As this process requires powerthat can drain the battery, the rate may be chosen based upon the falldetection performance and the device battery life. The trigger process405 is designed to minimize the number of non-fall events while it willdetect (pass) all possible falls, with the idea that the classifier willbe able to further discriminate among falls and non-falls that aredetected by the trigger 405. The role of the trigger is to minimize thenumber of calls to the classifier and by that save the powerconsumption.

Once the trigger 405 indicates an impact and a possible fall, the falldetector performs feature extraction 410. Various features may beextracted from the data collected around the time of the impact detectedby the trigger process 405. Some motion feature values may includeacceleration in three dimensions or these could be processed to producethe magnitude and a direction of the acceleration. Also changes inacceleration may be another feature that is extracted from the data.Further schemes to compute an impact value, changes in orientation, orheight are other motion features that may be extracted from the motiondata. In addition, physiological features such as changes in the heartrate or skin conductance as described above may be also calculated. Inthe heart rate example, the heart rate 3 seconds before the impact maybe determined, and the heart rate at the impact, as well as 4.5 secondsand 10 seconds after the impact may be determined. Then one or more ofthe four heart rate change values described above or other features maybe computed, for example, between the two values after the impact andbetween the value at impact and value at 10 sec after impact.

An optional feature of the feature extractor 410, may include detectingthat a feature or data underlying the feature is outside of a normalrange for a fall. This may be done, for example, by verifying that thesevalues are within certain ranges. When the data is not within thoseranges, it may indicate a non-fall or possibly a data error. When thisoccurs, the detected impact is ignored and not further processed. Thismay be considered a further refinement of the trigger 405 as the trigger405 is intended to be low complexity that is run at a higher rate. Thisoptional feature allows for reducing the need for running the classifierunnecessarily and will aid in extending the battery life of the falldetector.

Next, a classifier 415 receives the extracted features and makes adetermination regarding whether a fall has occurred. The classifier 415is optimized, but the classifier 415 will still classify a smallpercentage of real falls wrongly because the optimization process mayset the decision boundaries within the distribution as explained in FIG.1 and FIG. 2 . As described above a variety of different types ofmachine learning classifiers may be used. Typically a NBC classifier maybe used, but as discussed above a SVM, decision tree, random forest,neural networks including deep learning, logistic regression, k-NN,types of classifiers may also be used. In case of deep learning, thefeature extraction step 410 might be integral part with the classifier415, together constituting the (deep) network, as is known in the art.The classifier may be trained using a set of labeled data and a lossmodel. The training process proceeds to optimize the parameters of theclassifier by adjusting the paraments of the classifier until the erroror change in error reaches a certain threshold. Such models and trainingtechniques are known in the art.

Optionally, the fall detector may also perform testing for exceptions420 on the output of the classifier. For example, the classifier 415 mayindicate that a fall has occurred, but the fall detector may alsoreceive an indication that the user was, for example, walking or wavingtheir hand at the time of the event classified as a fall. In such acase, the fall detection may be rejected and no fall is indicated. Suchan approach may take advantage of other models that may be present inthe fall detector. If for example the fall detector is a smart watch,the smart watch may have a machine learning model that detects that auser is walking or waiving their hand. These models may also be run onthe data collected at the time of the suspected fall to determine, if inactuality other user behavior was occurring that was mis-classified as afall. In other embodiments, these sorts of machine learning models mayalso be developed directly to be used with the fall detection classifier415 to further improve the accuracy of the fall detector by excludingevents that are indeed non-falls.

Finally, the fall detector may report 425 the fall. This report may goto an external system or person to alert others that the user has fallenand may need assistance. The fall detector may also provide a visual oraudible alert to indicate to those near the user that the user hasfallen so that the user may be assisted as needed. It also may provide arecord of falls, either on the fall detector or on a remote device, forlater use by healthcare providers. The fall report 425 may include anoption for the user to cancel or revoke the fall alert.

In another embodiment, the classifier 415 may be a two stage classifier.In this approach, a first classifier is implemented that uses just themotion data and features. Outside of the area of confusion 215 (see FIG.2 ), this type of first classifier may be very accurate. Accordingly,when a fall or non-fall classification is highly certain, then theoutput of the first classifier is the output of the classifier 415. Thiscan be determined by the use of two thresholds. For example, when anevent is below a first threshold it is clearly a non-fall. When an eventis above a second threshold, it is clearly a fall. When the output ofthe first classifier is not certain, that is it is in between the twothresholds, then the classifier is undetermined. The output of the firstclassifier will be fall, non-fall, or undetermined. When the firstclassifier is undetermined then a second classifier using thephysiological data may be used to further clarify the classificationusing the physiological features. This can improve the classification ofevents that are in the confusion area 215. The first classifier istrained using only motion data, and the second classifier is trainedusing both the motion data and the physiological data, possibly subsetto those that have outcomes in the area of confusion. This approach maybe used when the physiological data may be noisy or have otherreliability or dropout issues. It avoids a mis-classification by asingle classifier when the physiological data is problematic, but themotion data alone provides a clear and reliable classification based ononly the motion data.

As discussed above, the heart rate may be measured using PPG or ECGsensors depending upon the location on the user's body and capability ofthe heart rate sensor in the fall detection device. Any current orfuture heart rate sensor that is compact and accurate enough may be usedto provide the heart rate data for the fall detector. Likewise, anycurrent of further skin conductivity sensor that is compact and accurateenough may be used to provide the skin conductivity data for the falldetection.

While the accuracy of motion only fall detection varies depending onwhere the motion sensors are on the users body, the incorporation ofphysiological data and features in the classification of falls canimprove the accuracy of the fall detection no matter the location on theusers body. Where the fall detector is on the wrist, arm, ankle, foot,or leg of the person that move much more than for example the torso ofthe user, the use of physiological data and features can greatly improvethe accuracy of fall detection in such locations.

The fall detector can be implemented as a stand-alone device or beintegrated into other user wearable devices such as a smart watch,fitness tracker, emergency alert device such as a pendant, etc. In anyof these embodiments, the fall detector device would have motionsensors, such as accelerometers or atmospheric pressure sensors toprovide the motion data for the classifier or be able to receive suchdata in a timely fashion from sensors located elsewhere on the usersbody. Further, these embodiments of the fall detector, would alsoinclude physiological sensors, such as heart rate sensor and/or skinconductivity sensors to provide the physiological data for theclassifier or be able to receive such data in a timely fashion fromsensors located elsewhere on the users body. For example, the user mayuse a smart watch with built in accelerometers, magnetometers, andatmospheric pressure sensors that provide the motion related data.Further, the user may wear a heart rate monitor strap across their chestto measure heart rate. The smart watch connects to the heart ratemonitor wirelessly to collect the heart rate data for use in theclassifier. Various other configurations of the sensors and processorsto process the sensor data may also be used.

FIG. 5 illustrates an exemplary hardware diagram 500 for the falldetector. The hardware diagram 500 may implement the fall detectionprocess described in FIG. 4 and indicate that a fall has occurred toanother system or to a person connected to the user. As shown, thedevice 500 includes a processor 520, memory 530, user interface 540,network interface 550, storage 560, motion sensor(s) 570, andphysiological sensor(s) 572 interconnected via one or more system buses510. It will be understood that FIG. 5 constitutes, in some respects, anabstraction and that the actual organization of the components of thedevice 500 may be more complex than illustrated.

The processor 520 may be any hardware device capable of executinginstructions stored in memory 530 or storage 560 or otherwise processingdata. As such, the processor may include a microprocessor, a graphicsprocessing unit (GPU), field programmable gate array (FPGA),application-specific integrated circuit (ASIC), any processor capable ofparallel computing, or other similar devices. The processor may also bea special processor that implements machine learning models, inparticular deep learning architectures.

The memory 530 may include various memories such as, for example L1, L2,or L3 cache or system memory. As such, the memory 530 may include staticrandom-access memory (SRAM), dynamic RAM (DRAM), flash memory, read onlymemory (ROM), or other similar memory devices.

The user interface 540 may include one or more devices for enablingcommunication with a user and may present information to users. Forexample, the user interface 540 may include a display, a touchinterface, a mouse, and/or a keyboard for receiving user commands. Insome embodiments, the user interface 540 may include a command lineinterface or graphical user interface that may be presented to a remoteterminal via the network interface 550.

The network interface 550 may include one or more devices for enablingcommunication with other hardware devices. For example, the networkinterface 550 may include a network interface card (NIC) configured tocommunicate according to the Ethernet protocol or other communicationsprotocols, including wireless protocols. Additionally, the networkinterface 550 may implement a TCP/IP stack for communication accordingto the TCP/IP protocols. Various alternative or additional hardware orconfigurations for the network interface 550 will be apparent. Thenetwork interface 550 may be used to transmit a fall detection alert toa remote user or system. Also, where the motion sensor(s) 570 and/or thephysiological sensor(s) 572 are separate from the fall detection device,the network interface 550 may facilitate receiving such data from theremote sensors.

The storage 560 may include one or more machine-readable storage mediasuch as read-only memory (ROM), random-access memory (RAM), magneticdisk storage media, optical storage media, flash-memory devices, orsimilar storage media. In various embodiments, the storage 560 may storeinstructions for execution by the processor 520 or data upon which theprocessor 520 may operate. For example, the storage 560 may store a baseoperating system 561 for controlling various basic operations of thehardware 500. The storage 562 may store instructions for detecting andreporting falls.

It will be apparent that various information described as stored in thestorage 560 may be additionally or alternatively stored in the memory530. In this respect, the memory 530 may also be considered toconstitute a “storage device” and the storage 560 may be considered a“memory.” Various other arrangements will be apparent. Further, thememory 530 and storage 560 may both be considered to be “non-transitorymachine-readable media.” As used herein, the term “non-transitory” willbe understood to exclude transitory signals but to include all forms ofstorage, including both volatile and non-volatile memories.

While the system 500 is shown as including one of each describedcomponent, the various components may be duplicated in variousembodiments. For example, the processor 520 may include multiplemicroprocessors that are configured to independently execute the methodsdescribed herein or are configured to perform steps or subroutines ofthe methods described herein such that the multiple processors cooperateto achieve the functionality described herein. Such plurality ofprocessors may be of the same or different types. Further, where thedevice 500 is implemented in a cloud computing system, the varioushardware components may belong to separate physical systems. Forexample, the processor 520 may include a first processor in a firstserver and a second processor in a second server.

While FIG. 5 shows a system with a processor carrying out all of thefunctions of the fall detector, some of the functions of the falldetector may be implemented directly on hardware. For example, thetrigger 405 may be implemented on dedicated hardware that is low powerand tailored to detecting impact events. This may be implemented with acircuit designed to specially carry out this function or may include alow power processor that is programmed to carry out this function. Also,as the classifier is implemented using machine learning techniques, theclassifier could be implemented using a circuit or processor optimizedto carry out machine leaning functions.

The fall detection device described herein provides a technologicalimprovement of current fall detection systems by using physiologicaldata to improve the detection of falls. Because a fall will quicklyproduce a physiological reaction in the person who falls, this may beused to improve the detection of falls. This is especially beneficial,when the fall detection device is worn on an area of the body whereother normal motion may lead to false fall detection alerts.

Any combination of specific software running on a processor to implementthe embodiments of the invention, constitute a specific dedicatedmachine.

As used herein, the term “non-transitory machine-readable storagemedium” will be understood to exclude a transitory propagation signalbut to include all forms of volatile and non-volatile memory.

Although the various exemplary embodiments have been described in detailwith particular reference to certain exemplary aspects thereof, itshould be understood that the invention is capable of other embodimentsand its details are capable of modifications in various obviousrespects. As is readily apparent to those skilled in the art, variationsand modifications can be affected while remaining within the spirit andscope of the invention. Accordingly, the foregoing disclosure,description, and figures are for illustrative purposes only and do notin any way limit the invention, which is defined only by the claims.

What is claimed is:
 1. A method for detecting a fall by a user wearing afall detector, comprising: detecting a trigger event identifying thetime location of a possible fall event in user data; extracting motionfeatures from motion data and physiological features from physiologicaldata from within a time window around the identified time location; anddetermining whether the detected trigger event is a fall by the user byinputting the at least one of the motion features and at least one ofthe physiological features into a classifier determining a firstphysiological data value at a first time before the trigger event;determining a second physiological data value at the trigger event;determining a third physiological data value at a third time after thetrigger event, wherein at least one physiological feature is based upona difference between two of the first, second, and third physiologicaldata values.
 2. The method of claim 1, wherein the motion data includesone of acceleration data, height data, angular velocity data, andacceleration data and height data.
 3. The method of claim 1, wherein themotion data includes data from an accelerometer.
 4. The method of claim1, wherein the physiological data include one of heart rate data, skinconductance data, and heart rate and skin conductance data.
 5. Themethod of claim 1, wherein the physiological features are based upon adifference between the first and second physiological values, the secondand third physiological values, the first and third physiologicalvalues, and second physiological value and one half the sum of the firstand third physiological feature.
 6. The method of claim 1, whereindetecting an impact based upon motion data further comprises determiningthat a change in acceleration over a specified time exceeds a thresholdvalue.
 7. The method of claim 1, further comprising determining thatextracted motion features are outside a specified normal range of valuesand then determining that the impact is not a fall by the user.
 8. Themethod of claim 1, further comprising when a fall is indicated,receiving an output from an exception machine learning classifier thatindicates that the impact is not a fall.
 9. The method of claim 1,wherein the machine learning classifier includes: a motion classifierthat determines whether the impact is a fall, a non-fall, orundetermined based upon the extracted motion features and a firstthreshold value and a second threshold value; and a physiologicalclassifier that determines whether the impact is a fall or a non-fallbased upon both the extracted motion features and the extractedphysiological features when the output of the motion classifier isundetermined.
 10. The method of claim 1, further comprising receivingthe physiological data from a remote sensor.